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AI 모델 85003

• GRaPE-2.5-Helios-GGUF↗

  SL-AI

• sdxs-512-dreamshaper-ExecuTorch↗

  software-mansion

• music_moods_classification↗

  StanislavKo28

  Music mood classification is a fundamental and versatile application in many various domains. Some possible use cases for music mood classification include: - music recommendation systems; - content organization and discovery; - radio broadcasting and programming; - music licensing and copyright management; - music analysis and research; - content tagging and metadata enrichment; - audio identification and copyright protection; - music production and creativity; - healthcare and therapy; - entertainment and gaming. The model is trained based on publicly available dataset of labeled music data — HWNAS Dataset — that contains 6930 sample 30-second audio files evenly split among 14 moods:

• Qwen3-Coder-30B-A3B-Instruct-AWQ-4bit-Q4_K_M-GGUF↗

  straino

• Vectorite-Pro-4B↗

  Sup2Doggie

• Mach-2-Additive-Medium-GGUF↗

  SyzygyResearch

• kanana-1.5-8b-instruct-2505-Safe-DPO↗

  Taewan123

• TeraTTSv2↗

  TeraSpace

  TeraTTSv2 is a self-contained ONNX Runtime text-to-speech release with selectable diffusion samplers, ten voice styles, Russian stress marking, and streamed audio output. This release uses the clean English/Russian 25-second teacher and its matching eight-step CFG-3 distilled student. > **Important — Russian stress is automatic.** Text inside ` … ` receives > stress markers automatically by default. Explicit `+` markers always win. > > **Important — cross-language prompts.** When an English reference voice is > speaking Russian, experiment with `duration_scale` below `1` (for example > `0.8`). It is usually a better starting point than the default `1`. > > **Recommended voices:** `ru_f1` and `ru_m5` are the preferred Russian voice > prompts.

• CodeLlama-70B-hf-GGUF↗

  TheBloke

• TinyLlama-1.1B-Chat-v0.3-GPTQ↗

  TheBloke